Fivetran vs Snowflake: 5 Critical Differences
Today, businesses all around the world are driven by data. This has led organizations to utilize every available online application, service, and even social platform to extract data to gain a deeper understanding of the changing market trends. This data goes through numerous complex transformations to be in analysis-ready form. Moreover, organizations need tools and technologies that can replicate and manage huge amounts of data from various sources in real-time. Fivetran is a data pipeline solution that eases the replicating process, and Snowflake is a fully-managed data storage and analysis platform that is utilized by businesses to store, manage, and analyze their ever-increasing data.
In this guide, we share in-depth details about Fivetran vs Snowflake: the key differences between the two to help you understand what they mean and how they can be leveraged for efficient analytics.
Table of Contents
Fivetran vs Snowflake
You will learn about Fivetran vs Snowflake on various parameters. The following parameters will be discussed in detail to have a better understanding and distinguish the two platforms:
Let’s start with an overview of the two.
Fivetran vs Snowflake: Overview
Fivetran is a data pipeline solution that can help you transform and centralize data to extract new, rich data in minutes. It also routinely keeps up with the API changes. This allows you to analyze data in real-time with auto-updated tables, columns, and rows.
Snowflake supports data lakes, data warehousing, data application development, data science, data engineering, and consumption of real-time / shared data and secure sharing. Snowflake is a fully managed SaaS platform that offers pre-built capabilities, including storage and compute separation, data sharing, data cloning, on-the-fly scalable compute, and third-party integrations support to extend the desired capabilities.
Fivetran vs Snowflake: Working Methodology
The working methodology of Fivetran is based on ELT. It basically has 3-steps, extract, load, and transform.
- Extract: In extract, raw data is collected from various sources, which must be replicated in the desired destination.
- Load: The raw data is replicated from the source and is loaded into the destination, which could be a data warehouse, data lake, etc.
- Transform: Data can be transformed as per the organization’s requirements once the data is replicated into the destination. Data is transformed in various ways to integrate and leverage it with many tools according to the business needs.
Snowflake works on the data warehousing concept. The primary objective of data warehousing is to have a centralized location where data is stored from various sources in analysis-ready form. Snowflake’s working model basically includes three tiers:
- Database Server: The data is loaded and stored in the bottommost layer, which is known as the Database Server/Data Warehouse.
- Analytics engine: It is present in the middle layer, which provides the platform for analysis and is also known as an OLAP server.
- Front-end client layer: The results after performing analysis, reporting, and data mining are visible to the user on the topmost front-end client layer.
Fivetran vs Snowflake: Personnel
Fivetran is an ELT solution, and it provides some pre-load transformation features; hence it is crucial for data engineers who want to ease out their data replication activities, as well as for anybody who is concerned about the same. It’s important to know the linkages between the data to carry out the data operations successfully. Although non-technical folks can easily replicate the data using managed data pipeline solutions but it’s good to have prior basic SQL and API knowledge.
Snowflake is administered by data engineer or database administrator (IT) but is significantly important to everyone in the organization since it is the centralized data source. Users (typically Analysts) can build their reports by transforming data according to their use cases. The admin of the Snowflake should be well versed with SQL and programming ability in languages such as Python, Java, Scala, etc., while the business users can easily work on the warehouse with basic SQL knowledge.
Fivetran vs Snowflake: Applications
The end applications or use cases of Fivetran and Snowflake from the user perspective are the same, but what differs is the fact that Fivetran plays a crucial part in “facilitating” the data while Snowflake plays a role in “storing and analyzing” the data.
- Storage: The data is collected from various sources and stored in a data warehouse, Snowflake, in this case, using an automated data pipeline.
- Reporting: Fivetran helps in facilitating real-time reporting because of its fast replication capabilities. Snowflake is built on a Spark engine which enables the system to run efficiently and build real-time reports.
- Analytics: Fivetran loads the data based on parameters like timestamps, which helps in loading the data efficiently and providing analysis-ready data. Due to Snowflake’s powerful engine, data analysis can be done on any scale to gain insights.
Fivetran vs Snowflake: Alternatives
Fivetran is a data pipeline solution with some pre-load transformation features. Despite supporting a wide range of data sources, Fivetran limits in certain features and parameters like complex pricing models, data ingestion, custom schema mapping, data scaling, data transformations, and support. It becomes hard for organizations that want easy-to-use UI platforms and cover a wide range of data replication scenarios. You can look deeply at Fivetran comparison details and its alternatives here. Hevo is one such alternative.
Hevo is a fully-managed Data Pipeline platform that offers seamless data replication and transformation. Its pre-load and post-load transformation capabilities can accommodate almost any use case. While moving data, you can easily transform all your data using both Python-based transformation scripts and drag-and-drop transformation blocks.
Snowflake is a fully managed data platform used for storage and analysis. Based on your selection, it leverages the cloud platform (AWS, Azure, GCP). Based on your use cases, you might be looking for an alternative. For example, if you want to employ advanced machine learning capabilities, then Databricks is one such solution, and it is also a fully managed data platform similar to Snowflake with some distinct features and functionalities. If you trust one particular cloud source, AWS, you can go for the Redshift data warehouse. You can deeply understand Snowflake alternatives here and choose the best option according to your use case.
Summing It Up
In this article, you got an in-depth analysis of Fivetran vs Snowflake: the key differences. A data pipeline solution becomes necessary if there are rapid changes in the source and frequent data replication needs to be done to meet the data demands of your product or marketing channel. You can free your engineering bandwidth from these repetitive & resource-intensive tasks by selecting Hevo’s 150+ plug-and-play integrations.
Visit our Website to Explore HevoSaving countless hours of manual data cleaning & standardizing, Hevo’s pre-load data transformations get it done in minutes via a simple drag-n-drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can simply run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form.
Want to take Hevo for a ride? Sign Up for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.
Share your experience of replicating data from Fivetran vs Snowflake! Let us know in the comments section below!
